Prefrontal executive function and D<b><sub>1</sub></b>, D<b><sub>3</sub></b>, 5-HT<b><sub>2A</sub></b> and 5-HT<b><sub>6</sub></b> receptor gene variations in healthy adults
Bibliographic record
Abstract
Objective: The Val158Met polymorphism of the catechol-O-methyltransferase gene has been demonstrated to be associated with prefrontal executive function explaining 4% of variance in perseverative errors on the Wisconsin Card Sorting Test (WCST). Studies suggest that dopamine D1 and D3 and serotonin 5-HT2A and 5-HT6 receptors may also be involved in prefrontal cognitive function and that genetic polymorphisms (D1 A-48G, D3 Ser9Gly, 5-HT2A T102C, and 5-HT6 T267C) of these receptors may be associated with brain glucose metabolism or neurophysiological function. The current study’s objective was to investigate whether executive function varies with these genetic variations. Methods: A sample of 216 healthy Han Chinese adults were measured with the WCST and genotyped for the 4 genetic polymorphisms. Results: Kruskal–Wallis tests showed a significant difference in WCST perseverative errors among the genotypes D3 Ser9Gly ( p = 0.009), 5-HT2A T102C ( p = 0.038) and 5-HT6 T267C ( p = 0.010), but not in the genotype D1 A-48G. Multiple regression analysis for the WCST natural logarithm values (i.e., for fulfilling the normal distribution requirement) showed that subjects’ perseverative errors were significantly influenced by D1 A-48G, D3 Ser9Gly, 5-HT2A T102C and 5-HT6 T267C polymorphisms after adjustment of other variables. Conclusion: The preliminary data suggest that D1, D3, 5-HT2A and 5-HT6 genetic mutations may influence prefrontal executive cognition in healthy adults. Further studies in larger samples with other ethnicities or in mentally ill patients are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".